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CS6534 Guided Studies Crowd Flow Analysis

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Title: CS6534 Guided Studies Crowd Flow Analysis


1
CS6534 Guided StudiesCrowd Flow Analysis
  • Supervised by Dr. Hau-San WONG
  • Prepared by Kam-fung YU
  • (51150118)

2
Background challenge
  • Video Surveillance System are widely used for
    monitoring
  • Performance is good as the number of object for
    detection is small (Spatial variation is small)
    and
  • The change over time is small (Temporal variation
    is small)
  • BUT
  • A challenge for crowd flow
  • The number of objects is in the order of 102103
  • The change of the scene is very fast

3
Related works
  • Based on Tracking of Individuals
  • Shape and Color Model of Individuals
  • Trajectories of Points
  • Boundary Contour
  • xt Slices of Spatio-temporal Video Volume
  • People Counting in the Crowd

4
Shape color model of individuals
3D human model
  • Models human shape by using 3D model
  • Data-driven Markov chain Monte Carlo (DDMCMC)
  • Iterate an optimized solution

T. Zhao et. al., Bayesian Human Segmentation in
Crowded Situations, IEEE CVPR03, 2003.
5
Trajectories of points
  • Similar method with Shape Color Model
  • Use some simple feature, such as corner of an
    object, to extract points probabilistically
  • Clustering the points into independently moving
    entities, cluster

Shape and Color Model
Trajectories of Points
G. Brostow et. al., Unsupervised Bayesian
Detetcion of Independent Motion in Crowds, IEEE
CVPR, 2006.
6
Boundary contour
  • Use of low-interest points to detect the object
    clustering
  • Select by the high temporal and spatial
    discontinuity
  • Outline the object by joining edges

Clustered object
Sample Scene
P. Tu et. al., Crowd Segmentation through
Emergent Labeling, In ECCV Workshop SMVP, 2004
7
Xt sclies of spatio-temporal video volume
  • Scan interesting lines over a certain frames,
    xt-slice
  • Use the Hough transform to detect movement in the
    xt slices



5 corresponding xt slices
Sample Scene with 5 lines
Hough transform
P. Reisman, Crowd Detection in Video Sequences,
IEEE Intelligent Vehicles Symposium, 2004.
8
People counting in the crowd
  • Clustering of some feature points by their motion
  • Estimate the number of people by the number of
    cluster

A result of clustering on two video scene
V. Rabaud et. al., Counting Crowded Moving
Objects, IEEE CVPR, 2006
9
Limitations on tracking of individuals
  • Involes Iteration
  • Convergence Decease as Number of Objects Increase
  • Large Computational Time
  • High of Computational Power
  • Difficulty to implement on Real Time Monitoring
    System

10
Our approach
  • Proposed by Saad Ali Mubarak Shah in 2007
  • Individual Flow ? Global Optical Flow
  • Tracking Individuals ? Measuring Global
    Quantities
  • Using Fluid Dynamics to treat the problem
  • Global QuantitiesFinite Time Lyapunov Exponents
    Field (FTLE), Lagrangian Coherent Structures(LCS)
  • Expect a Higher Faster Algorithm in Performance

11
Algorithmic outline
12
Optical flow
  • 16x16 size block
  • Displacement vector x
  • p frames for 1 mean field
  • q mean field for 1 block mean field

S. Ali, M. Shah, A Lagrangian Particle Dynamics
Approach for Crowd Flow Segmentation and
Stability Analysis, CVPR May, 2007
13
Algorithmic outline
14
Flow map
  • Launch a set of particles over the optical flow
    field
  • Solve a flow map for a time period T p?q frames
  • Interpolation a cubic velocity equation by 4th
    order Runge-Kutta-Fehlberg algorithm (RK4)
  • ??x, ?y are used to record the x and y coordinate
    at each initial position launched after time

Flow map of x-particle
Flow map of y-particle
S. Ali, M. Shah, A Lagrangian Particle Dynamics
Approach for Crowd Flow Segmentation and
Stability Analysis, CVPR May, 2007
15
Algorithmic outline
16
Ftle Field
  • Compute the four spatial derivates
  • Plug into the Cauchy-Green deformation tensor
  • The largest finite time Lyapunov exponent with
    the maxmum eigenvalue ?max of the tensor and the
    period Tp?q frames

FTLE Field Plot
S. Ali, M. Shah, A Lagrangian Particle Dynamics
Approach for Crowd Flow Segmentation and
Stability Analysis, CVPR May, 2007
17
Algorithmic outline
18
lcs
FTLE Field Plot
S. Ali, M. Shah, A Lagrangian Particle Dynamics
Approach for Crowd Flow Segmentation and
Stability Analysis, CVPR May, 2007
19
segmentation
  • This process involves two stages
  • Cut spatially into different region by the ridges
    in FTLE
  • Use the Lyapunov divergence to decided two
    segment merge or not

1st stage
2nd stage
S. Ali, M. Shah, A Lagrangian Particle Dynamics
Approach for Crowd Flow Segmentation and
Stability Analysis, CVPR May, 2007
20
Algorithmic outline
21
Flow instability detection
  • Flow instability is defined as the change in the
    number of flow segments with respect to time

New segment
Current segment
S. Ali, M. Shah, A Lagrangian Particle Dynamics
Approach for Crowd Flow Segmentation and
Stability Analysis, CVPR May, 2007
22
Capabilities
  • Capable for monitoring thousands of objects
    simultaneously
  • Get rid of number of people constrain
  • Capable for monitoring flow in any orientation
  • Obtain same result under any rotation
  • Capable for new segment detection over time
  • Locate the increase or the decrease of segments
    over time

23
Potentials
  • Potential for flow control or city design
  • Making immediate decision for crowd flow
  • Facilitate on the planning of city streets,
    traffic flow, overhead, bridges and passageways
  • Potential for flow pattern recognition
  • Extraction of various flow pattern
  • Flow pattern solution space construction for a
    given static scenery
  • Flow pattern bases finding

24
limitations
  • Limitation on crowd density
  • Degraded as crowd density is low
  • Worse at only a number of objects
  • Limitation on a large number of many-fold
    dynamics flow
  • Too many segments (too noisy) on the scene
  • Hard to merge segment
  • Limitation on a rapid unstable flow
  • Hard to retrieval information from rapid changing
    flow
  • Too slow to capture the information

25
Further suggestions
  • Find out the critical crowd density for an
    acceptable performance
  • Finding out a method that can undergo
    segmentation under a noisy domain
  • Designing a rapid flow capturing algorithm
  • Finding out the possible flow patterns on given
    static scenery
  • Find out the flow patterns solution space and
    bases

26
conclusion
  • In this guided study, we studied about various
    kinds of methods and the Lagrangian Dynamics in
    solving the crowd segmentation problem.
  • We also realized the capabilities, potentials and
    limitations .
  • We finally suggested some possible direction for
    future studies.

27
references
  • Z.N. Li, M.S. Drew, Fundamentals of Multimedia,
    NJ Pearson Education Hall, 2004
  • P.E. Mattison, Practical Digital Video with
    programming examples in C, NY John Wiley Sons
    Inc, 1994
  • L. Perko, Differential Equations and Dynamical
    Systems 3rd Ed., NY Springer, 2001
  • Intel Corporation, Open Source Computer Vision
    Library, Reference Manual, USA Intel
    Corporation, 2001
  • S. Ali, M. Shah, A Lagrangian Particle Dynamics
    Approach for Crowd Flow Segmentation and
    Stability Analysis, CVPR May, 2007
  • S. C. Shadden, Lagrangian Coherent Structures
    Analysis of time-dependent dynamical systems
    using finite-time Lyapunov exponents,Available
    Online http//www.cds.caltech.edu/shawn/LCS-tut
    orial/, Last update 15th April, 2005
  • P. Reisman, Crowd Detection in Video Sequences,
    IEEE Intelligent Vehicles Symposium, 2004.
  • T. Zhao et. al., Bayesian Human Segmentation in
    Crowded Situations, IEEE CVPR03, 2003.
  • P. Tu et. al., Crowd Segmentation through
    Emergent Labeling, In ECCV Workshop SMVP, 2004.
  • G. Brostow et. al., Unsupervised Bayesian
    Detetcion of Independent Motion in Crowds, IEEE
    CVPR, 2006.
  • D. Yang et. al., Counting People in Crowds with
    a Real-Time Network of Simple Image Sensors,
    ICCV, 2003.
  • V. Rabaud et. al., Counting Crowded Moving
    Objects, IEEE CVPR, 2006.
  • E. Rosten and T. Drummond, Machine learning for
    high-speed corner detection, Europe Conference
    on Computer Vision, May 2006.
  • C. Tomasi and T. Kanade, Detection and tracking
    of point features, Technical Report
    CMU-CS-91-132, Carnegie Mellon University, April
    1991.
  • S. Ali, Crowd Flow Segmentation Stability
    Analysis, Available Online http//www.cs.ucf.ed
    u/sali/Projects/CrowdSegmentation/index.html ,
    Last visited 30th Nov, 2008

28
Thank you
  • Department of Computer Science
  • City University of Hong Kong
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